Introduces two master feature flags that select which UI surface is
exposed, replacing the previous "delete legacy pages" approach with a
softer isolation:
- STUDIO_MODE_ENABLED (default false) — legacy OCR-debug surface
- RAG_PIPELINE_ENABLED (default true) — new doc-centric ingestion + viz
At least one master must be enabled (validated server-side at startup).
Sub-flags (inspect / linked / ask) are effective only when the RAG
pipeline master is on.
CHUNKS_MODE_ENABLED renamed to LINKED_MODE_ENABLED in anticipation of
T3 (Linked view replaces the Chunks tab). The DocMode union value
'chunks' is preserved for now and will be renamed in T3 alongside the
route segment, to keep this PR scoped.
Router-level guard added: requests to a route whose surface is disabled
are redirected to the other surface's landing page (or /home as a
defensive fallback). Logic extracted into a pure resolveSurface helper
with full test coverage.
i18n strings that pointed users to "Studio" rewritten to be surface-
agnostic ("from the library" / "depuis la bibliothèque") since Studio
is hidden by default in 0.6.1.
Backend:
- infra/settings.py: add studio_mode_enabled + rag_pipeline_enabled;
rename chunks_mode_enabled → linked_mode_enabled; add at-least-one
master validation in __post_init__
- api/schemas.py: HealthResponse exposes both master flags + renamed
sub-flag
- main.py: health endpoint wires the new fields
- tests: surface-flag + renamed sub-flag assertions
Frontend:
- features/feature-flags/store: add studioMode + ragPipeline registry
entries; rename chunksMode → linkedMode; sub-flags now require
ragPipeline enabled; modeFlags() maps linkedModeEnabled → key 'chunks'
(transitional)
- shared/routing/resolveSurface: pure helper + tests
- app/router: beforeEach guard consumes resolveSurface
- shared/i18n: Studio-pointing strings rewritten (en + fr) + test sync
- features/reasoning: stale "from StudioPage" comment generalized
GET /api/analyses?documentId=X retournait toutes les analyses (le query
param était ignoré). Le frontend prenait alors la première COMPLETED de
la réponse, qui pouvait être celle d'un autre doc → bboxes d'un autre
doc projetées sur l'image en cours.
Backend
- analysis_repo: nouvelle méthode find_by_document(document_id, limit, offset)
- analysis_service: expose find_by_document
- api/analyses: GET /api/analyses accepte ?documentId=... (alias Pydantic
pour respecter la règle ruff N803). Si présent, filtre via la nouvelle
méthode; sinon comportement inchangé.
- test: test_list_analyses_filtered_by_document vérifie le routing vers
find_by_document quand le query param est fourni.
Frontend (defensive)
- DocInspectTab / DocAskTab: filtre client-side
analyses.find(a => a.documentId === requestedId && a.status === 'COMPLETED')
pour rester safe même si un backend antérieur ignore le param.
The frontend was wired against /api/documents/{id}/chunks/* (canonical
doc-centric chunkset) but the backend never exposed those routes — the
chunk tab in the doc workspace 404'd. The domain entities (Chunk,
ChunkEdit, ChunkPush) and persistence repos already existed since #205;
what was missing was the service + API layer that connects them.
ChunkService owns all canonical chunkset invariants (sequence ordering,
soft-delete + audit log atomicity) and shares the chunker port with
AnalysisService so chunking strategy stays a single implementation.
AnalysisService grew a duck-typed promoter hook that copies the chunks
of the first successful analysis into the canonical chunkset. The hook
is idempotent so subsequent ad-hoc analyses (Studio / OCR Debug) never
overwrite hand-edited state.
Routes added (all additive, /api/documents prefix):
GET /{id}/chunks
POST /{id}/chunks
PATCH /{id}/chunks/{chunkId}
DELETE /{id}/chunks/{chunkId}
POST /{id}/chunks/{chunkId}/split
POST /{id}/chunks/merge
POST /{id}/rechunk
GET /{id}/tree
GET /{id}/diff?store=...
POST /{id}/chunks/push
- domain : StoreKind.NEO4J ajouté à l'enum
- service : validation config par kind étendue (Neo4j requires non-empty index_name)
- frontend : Neo4jConfigForm (index_name + database optionnelle), dispatcher StoreConfigForm, dropdown kind, i18n FR/EN
- tests service : 2 cas Neo4j (create OK + missing index_name 422)
Auth (URI/user/password) reste pilotée par les variables d'environnement
globales (cf. infra/neo4j.py) — la config par store ne porte que les
paramètres routables (index, database).
Couche service entre l'API et les repos SQLite :
- list_stores avec enrichissement document_count via document_store_links
- get/create/update/delete avec validations
- Slug normalisé lowercase, motif kebab-case strict
- Unicité name + slug (409 sur collision)
- Single-default invariant via clear_default_except
- Validation config par kind (OpenSearch require index_name) — extensible
- Delete refusé sur le store seedé "default" et sur tout store avec liens
- Erreurs typées (Validation/NotFound/Conflict) avec hint http_status
Étend le repo avec les méthodes manquantes pour le CRUD :
- find_by_name : valider unicité name au create/update
- update : remplace les champs mutables (id et created_at intacts)
- clear_default_except : promotion mono-default lors d'un changement de default
- delete : retourne True/False selon suppression effective
Tests dédiés sur chacune des nouvelles méthodes.
Backend
- HealthResponse exposes inspectModeEnabled / chunksModeEnabled /
askModeEnabled (additive; defaults true). main.py /api/health
populates them from settings.
- infra/settings.py: three new env-var-driven booleans (defaults true)
parsed in from_env() like the existing reasoning_enabled flag.
- tests/test_api_endpoints.py: extra assertion that /api/health
surfaces the three new fields with their defaults.
Frontend — flag store
- features/feature-flags/store.ts: FeatureFlag union extended with
inspectMode / chunksMode / askMode. New entries in featureRegistry
are gated on context fields populated from health. Missing fields
fall back to true so a frontend pointed at an older backend keeps
every mode visible.
- store gains a modeFlags() helper returning Record<DocMode, boolean>
so the routing guard does not need to know the FeatureFlag union.
Frontend — routing
- shared/routing/resolveMode.ts: pure resolver. If the requested mode
is enabled, return it; else first enabled in priority ask > chunks
> inspect; else null.
- app/router/index.ts: beforeEach guard scoped to ROUTES.DOC_WORKSPACE.
Disabled mode → rewrite ?mode= to the first enabled one. All three
off → redirect to /docs?reason=no-mode-enabled.
Frontend — flash
- pages/DocsLibraryPage.vue: shows a banner when ?reason=no-mode-enabled
is set. #211 will move this into the proper library page banner.
- i18n flags.allModesDisabled added in fr + en.
Tests
- shared/routing/resolveMode.test.ts (6 cases): every (requested,
enabled) combination including all-disabled, priority order,
missing requested.
- features/feature-flags/store.test.ts: three new cases covering the
new fields in /api/health, fall-back-to-true on missing fields, and
modeFlags() shape.
Refs #210
CLI script to migrate existing tenants to the data model introduced
by #202-205. Idempotent and resumable — re-running is a no-op.
tools/migrate_06.py
- Step 1: backfill_lifecycle — infers documents.lifecycle_state from
analysis_jobs.status + chunks_json presence
Failed analyses, no completed -> Failed
Completed + chunks_json -> Chunked
Completed, no chunks_json -> Parsed
Otherwise -> Uploaded
- Step 2: materialize_chunks — promotes the latest chunks_json blob
for each doc into rows in the new chunks table. Stable id derivation
via uuid5(NAMESPACE_OID, '<doc>|<seq>|<text>') so re-running lands
on the same ids
- Step 3: backfill_links — for any doc that has chunks materialized,
creates a (doc, default-store) link in Ingested state with a freshly
computed chunkset_hash. Treats 'chunks exist' as proxy for 'doc was
ingested into the legacy single index' — sufficient for the typical
pre-0.6.0 deployment, with OpenSearch reindex documented separately
- Step 4: reaggregate — applies #203's aggregate_lifecycle rule so
doc-chunked transitions to Ingested
Persistence
- migration_progress table for resumability + per-step idempotency
CLI
python -m tools.migrate_06 # full migration
python -m tools.migrate_06 --dry-run # plan only, no writes
python -m tools.migrate_06 --only-step <name> # rerun a phase
Tests
- 9 tests: inference rule per (completed, failed, chunks_json) tuple,
end-to-end migration on a hand-built three-doc snapshot, idempotency
(second run = no writes), --dry-run writes nothing, deterministic
chunk ids across reruns
Refs #206
The data and domain layers for the chunks editor (#219-224 in 0.6.0).
Chunks were previously stored as a JSON blob in analysis_jobs.chunks_json;
this commit makes them first-class persisted entities with stable IDs,
soft-delete, and an immutable audit log.
Domain
- Chunk: persistent entity with id, document_id, sequence, text,
headings, source_page, bboxes, doc_items, token_count, timestamps,
deleted_at (soft delete)
- ChunkEdit: immutable audit row (action, actor, at, before, after,
parents, children, reason)
- ChunkPush: snapshot of which chunk_ids landed in which store at push
- ChunkEditAction enum: insert/update/delete/merge/split
- domain/chunk_editing.py: pure operations on a chunkset (insert,
update, delete, merge, split). Each returns a new chunkset and the
affected chunk(s); errors raise ChunkEditingError.
Persistence
- Three new tables: chunks, chunk_edits, chunk_pushes (FK + indexes)
- SqliteChunkRepository (insert, insert_many, update, soft_delete,
find_for_document, find_by_id; respects deleted_at)
- SqliteChunkEditRepository (append-only audit log; paginated reads
ordered newest-first; per-chunk history)
- SqliteChunkPushRepository (per-(doc, store) latest snapshot)
Ports
- ChunkRepository, ChunkEditRepository, ChunkPushRepository protocols
added to domain/ports.py
Tests
- 17 tests for the pure chunk-editing operations covering insert /
update / delete / merge / split, sequence shifts, lineage, error
paths (out-of-range, missing id, deleted target, cross-document)
- 11 tests for the three repositories: round-trips, soft-delete
filtering, history ordering, lineage round-trip, cascade-delete with
document, find_latest semantics
Service orchestration (ChunkEditingService — atomic chunk + audit
write) and the API endpoints land in a follow-up commit on the same
feature branch / next release. The data + domain foundation here is
what unblocks #219-224.
Refs #205
Adds the pure-domain hash function that summarises a chunkset for
stale-detection purposes. Recorded on each DocumentStoreLink at push
time (#203 ships the column slot); compared against the recomputed
current hash to flip a link to Stale when the source has drifted.
domain/hashing.py
- chunkset_hash(chunks: Iterable[ChunkResult]) -> str
- SHA-256 over (text, source_page, headings) per chunk
- Excludes bboxes / doc_items / token_count by design
- 0x1F separator between chunks defends against the join-attack
(split A+B vs concat AB)
Tests
- 9 tests: determinism, sensitivity per included field, exclusion of
rendering-only fields, join-attack resistance, order sensitivity,
empty-input invariant
- Locked fixture: a hand-built 3-chunk input has a fixed expected hash;
CI fails loud if anyone changes the canonical inputs without updating
the fixture deliberately (and the release notes)
Service integration (recompute on chunk write, set on push) lands with
#205 once chunks are first-class — direct integration on the legacy
chunks_json path is deliberately deferred to keep #204 focused.
Refs #204
Introduces the data layer for multi-store ingestion. Documents can now
live in multiple stores, each with its own Ingested/Stale/Failed state.
The doc-level lifecycle (#202) becomes the aggregate over all per-store
links, computed by a pure domain function.
Domain
- Store entity (name, slug, kind, embedder, config, is_default)
- DocumentStoreLink entity with mark_ingested / mark_stale / mark_failed
helpers
- StoreKind and DocumentStoreLinkState enums
- aggregate_lifecycle(): pure function — Failed > Stale > Ingested
> fallback (the doc's pre-link Uploaded/Parsed/Chunked state)
Persistence
- New tables 'stores' and 'document_store_links' with the right indexes
(doc_id, store_id, state) and a UNIQUE (doc, store) on the link
- Default 'opensearch' store seeded idempotently in init_db, embedder
pulled from DEFAULT_EMBEDDER (fallback bge-m3)
- SqliteStoreRepository (find_by_slug, find_by_id, get_default, …)
- SqliteDocumentStoreLinkRepository with ON CONFLICT … DO UPDATE upsert
Ports
- StoreRepository and DocumentStoreLinkRepository protocols added
Tests
- 14 new tests: seed idempotency, insert/find round-trips, UNIQUE
constraint, cascade delete with the document, every link state
round-trips, aggregation rule with all branches
Refs #203
Adds a first-class lifecycle state to every document, distinct from
AnalysisJob.status. The lifecycle describes the document as a whole and
is the foundation for the doc-centric pivot in 0.6.0.
Domain
- DocumentLifecycleState enum (Uploaded/Parsed/Chunked/Ingested/Stale/Failed)
- Document.lifecycle_state and lifecycle_state_at fields
- Document.transition_to() validates against a transition table
(domain/lifecycle.py) and returns a DocumentLifecycleChanged event
- InvalidLifecycleTransitionError on disallowed transitions
Persistence
- ALTER TABLE documents to add the two columns (default 'Uploaded')
- New index idx_documents_lifecycle_state for filter perf
- _COLUMN_MIGRATIONS refactored to support multiple tables
- _POST_MIGRATION_DDL list for indexes on freshly-added columns
- SqliteDocumentRepository.update_lifecycle()
Services
- AnalysisService drives transitions on parse / chunk / re-chunk / fail
via _transition_document(); idempotent and resilient (logs WARN and
continues if a stale state is somehow encountered)
API
- DocumentResponse exposes lifecycleState + lifecycleStateAt
(additive — existing 'status' field kept for backwards compat)
Frontend
- Document type extended with lifecycleState and lifecycleStateAt
- DocumentLifecycleState union literal mirroring the backend enum
Tests
- 24 new tests in test_lifecycle.py covering transitions, idempotency,
invariant preservation, and event emission
- test_repos.py: round-trip + every-enum-value check + update_lifecycle
- test_chunking.py: rechunk path now mocks document_repo correctly
Refs #202
Two patterns in Docling's serialization were mirrored 1:1 by the graph
projection and produced node explosions on real documents:
- An InlineGroup (paragraph of mixed style runs) emits one `groups[]`
entry plus N `texts[]` runs. Naive iteration created one Paragraph
node per run.
- A Picture's `children` carry internal text labels extracted by the
layout model (flowchart boxes, chart axis labels, diagram callouts).
Each child became its own Paragraph node, drowning the figure.
`build_collapse_index` (in the shared `infra.docling_tree` helper) now
returns the `skip_refs` set + `inline_meta` overrides for both cases.
The Neo4j `tree_writer` and the in-memory `docling_graph` consume the
same index, so both projections stay in sync.
InlineGroups are projected as a single :Paragraph carrying the
concatenated text and the union of children's provs (re-indexed).
Pictures keep their :Figure node and prov; their descendants are
dropped. Captions live in the picture's separate `captions` field, not
in `children`, so they are unaffected.
* docs: rename Clean Architecture → Hexagonal Architecture (ports & adapters)
Le backend suit le pattern ports & adapters (ports dans domain/ports.py,
adaptateurs dans infra/), pas Clean Architecture au sens Uncle Bob.
Aligne la terminologie dans README, docs/architecture.md, ADR guide,
audit master, fiche audit 01, et la nav mkdocs.
Les noms de fichiers et la commande /audit:clean-architecture restent
stables pour preserver les liens croises et les skills existants.
* feat(settings): add paste-image size/type limits surfaced via /api/health
Introduces MAX_PASTE_IMAGE_SIZE_MB (default 10) and
PASTE_ALLOWED_IMAGE_TYPES (default image/png,image/jpeg,image/webp)
env vars so the upcoming Verify-mode clipboard-paste handler can
validate client-side against the same limits the backend enforces.
Follows the existing MAX_FILE_SIZE_MB pattern. Ships the accepted
design doc at docs/design/195-copy-paste-image-verify-mode.md.
Refs #195
Propagate Docling `self_ref` through PageElement so bboxes and graph nodes
share a stable identity. Add a Document/Graph mode switch to the reasoning
workspace; selecting a node highlights its bbox (numbered badge, focus ring,
optional dim of non-visited) and clicking a bbox re-centers the graph.
Backend — live runner
- New `POST /api/documents/:id/rag` endpoint. Loads `document_json` from
SQLite, reconstructs the DoclingDocument, wraps the model id in
`ModelIdentifier(ollama_name=...)`, and calls `agent._rag_loop`
off-thread (blocking sync call). Returns a `RAGResult` in the shape
the existing v1 import path already consumes, so the frontend overlay
is fully reused.
- `_rag_loop` is private upstream; we call it because `run()` wraps the
answer in a synthetic DoclingDocument and drops the iteration trace.
- Settings: `RAG_ENABLED`, `OLLAMA_HOST`, `RAG_MODEL_ID`. Router mounts
unconditionally; handler 503s when the flag is off or deps aren't
installed. `rag_available` surfaced in `/api/health`.
- Maps known docling-agent bugs to readable HTTP errors: 502 with
"the model couldn't produce a parseable answer" when `_rag_loop`
raises `IndexError` from `find_json_dicts([])[0]` after 3 + 3
rejection-sampling retries (model-dependent).
- Tests: 11 cases (flag off, query empty, no analysis, happy path,
model_id wrap, Ollama env, IndexError → 502, other errors → 500,
deps missing → 503).
Backend — bug fix
- Default `BATCH_PAGE_SIZE` flipped from `10` to `0` to match the
dataclass default. The old default silently dropped `document_json`
(see `domain/services.merge_results`) for any doc > 10 pages, which
broke the reasoning tunnel. Set `BATCH_PAGE_SIZE>0` explicitly on
memory-constrained deploys if batching is wanted.
Frontend — runner UX
- `features/reasoning/api.ts:runReasoning()` — POST wrapper.
- `RunReasoningDialog.vue` — query textarea + optional model_id
override. Blocks close while running, 20-40s loading state,
synthesises a sidecar-shaped envelope so the panel surfaces query +
model the same way an imported trace would.
- `ReasoningWorkspace.vue` — primary "Run reasoning" button; "Import
trace" relegated to ghost secondary.
- Store: `runDialogOpen`, `running`, `setRunning`.
Frontend — answer polish
- Answer rendered through `marked` + DOMPurify (models emit markdown
lists; `pre-wrap` rendered them as plain "1. …" strings).
- Dedicated answer block with orange border, "ANSWER" label, "Copy"
button (clipboard + "Copied ✓" feedback).
- IterationCard: drop the duplicate `response` block (the main answer
is authoritative); style reasons equal to `"fallback"` (docling-agent
`select_from_failure` placeholder) as italic muted "— no structured
rationale".
Frontend — node details contents
- Clicking a SectionHeader (or any node with compound children) lists
its contained elements in `NodeDetailsPanel` under a new "Contents"
block. Children come from the same `parentMap` used for Cytoscape
compound parenting (explicit PARENT_OF + synthetic section scope),
inverted once and cached as a computed.
- Click a child row → pan the viewport to it + swap the selection.
Housekeeping
- `cytoscape-navigator` removed from `package-lock.json` (follow-up
from the minimap removal in the previous commit).
Adds the `docling-agent` reasoning-trace viewer as a Studio tunnel, per
`docs/design/reasoning-trace.md`. Users pick an analyzed document, import
a RAGResult JSON, and the iterations are overlaid on the document graph.
Graph source is decoupled from Neo4j: a new pure builder
(`infra/docling_graph.build_graph_payload`) reads `document_json` from
SQLite and emits the same Cytoscape-shaped payload that `fetch_graph`
returns from Neo4j. Neo4j stays exclusive to the Maintain ingestion
pipeline. Shared DoclingDocument helpers live in `infra/docling_tree.py`
so TreeWriter and the builder can't drift on label taxonomy or tree walks.
Also removes the Cytoscape minimap (cytoscape-navigator) from GraphView:
second render instance hurt perf on large documents for no UX win.
Backend
- new `GET /api/documents/:id/reasoning-graph` (SQLite-only)
- new `infra/docling_tree.py`, `infra/docling_graph.py`
- `analysis_repo.find_latest_completed_by_document`
- tests: `test_docling_graph.py` (builder), `test_graph_api.py` (endpoint)
Frontend
- `features/reasoning/` — store, overlay, types, panel, import dialog,
workspace, doc picker
- new `ReasoningPage` + `/reasoning` and `/reasoning/:docId` routes
- `GraphView` gains a `fetcher` prop so reasoning can inject the
SQLite-backed fetcher while Maintain keeps using the Neo4j one
- drops minimap (nav container, dep, CSS)
- legend filters + section parenting extracted for reuse
- i18n base strings (FR + EN)
The `doc_items` field was added to `ChunkResult` earlier in the
feature branch (used by ChunkWriter for DERIVED_FROM edges), but the
test fixture was never updated. CI caught it now that the PR is open.
Fixes: tests/test_chunking.py::TestChunkResult::test_serializable
Previous query chained 6 OPTIONAL MATCH clauses for edges with no
intervening WITH collect(), producing a cartesian product. At 6 pages
(~60 elements, ~300 edges) Neo4j hit 102% CPU and hung > 5min.
Rewritten with one CALL {} subquery per node/edge type: each block
returns a single row with its collected list — no multiplication across
types. 6-page doc now returns in 213ms (was: no return).
Python reshape code (queries.py:137-210) untouched — record keys and
edge map shape preserved.
Refs: https://neo4j.com/developer/kb/using-subqueries-to-control-the-scope-of-aggregations/
ChunkWriter mirrors chunks into Neo4j after OpenSearch indexing, creating
HAS_CHUNK edges and DERIVED_FROM back-references to the source Elements
(via doc_items propagated from the local chunker).
Graph API: GET /api/documents/{id}/graph returns a cytoscape-shaped
payload with nodes + edges for Document / Element / Page / Chunk.
Hard cap at 200 pages returns HTTP 413 per design §8.4.
Frontend: new Graph tab in Studio results, rendered with Cytoscape.js +
dagre layout (lazy-loaded, ~175 KB gz). Legend, node styling per element
label, directional edges styled per edge type.
README gains a Neo4j section with the schema, three demo Cypher
queries, and env vars. Backend tests skip cleanly when the neo4j python
package is not installed locally.
Refs #186
Serialize a DoclingDocument to a Neo4j graph: Document + Page + Element
nodes with dynamic specific labels (SectionHeader, Paragraph, Table,
Figure, …), plus HAS_ROOT / PARENT_OF / NEXT / ON_PAGE edges. Replace-on-
write for idempotent re-ingestion.
The reader returns the verbatim document_json stored on the Document
node — reconstruction from graph nodes is deferred to v0.6.
Wired into AnalysisService._finalize_analysis: runs after conversion,
degrades gracefully by default, fails fast when neo4j_required is set.
Refs #186
Add Neo4j as an optional graph-native storage layer (ingestion profile).
Introduces infra/neo4j with a singleton async driver wrapper and an
idempotent bootstrap of constraints + indexes, wired into the FastAPI
lifespan. Integration tests skip when no live Neo4j is reachable.
Refs #186
Hybrid approach: reuse LocalChunker to chunk the DoclingDocument JSON
returned by Serve, so chunking works identically in both local and
remote modes without calling Serve's chunk endpoint.
Backend:
- _build_chunker() always returns LocalChunker (remove engine guard)
- Use docling-core[chunking] extra for required dependencies
- Skip client-side batching in remote mode (Serve manages its own
resources, and batching discards document_json needed for chunking)
- Fix Serve form fields: remove generate_page_images (not a Serve
field), use repeated form keys for to_formats and page_range
- Log Serve error response body on 4xx/5xx for diagnosis
- Fix FastAPI 204 DELETE routes missing response_model=None
Frontend:
- Update chunking feature flag to enable Prepare UI in remote mode
Closes#51
- Move DEFAULT_PAGE_WIDTH/HEIGHT to domain/value_objects.py and import in both converters
- Add opensearch_default_limit to Settings (configurable via OPENSEARCH_DEFAULT_LIMIT env var)
- Pass settings.conversion_timeout to ServeConverter, removing independent _DEFAULT_TIMEOUT
- Update OpenSearchStore to accept default_limit from Settings via constructor
Default value of 5 is now in the application code (settings.py) instead
of only in the Docker image ENV. Consistent across all deployment modes
(dev local, Docker, tests). Aligned docker-compose files and docs.
- Conditionally mount ingestion router only when OpenSearch + embedding are configured
- Add `ingestionAvailable` field to /api/health response
- Add `ingestion` feature flag to frontend (hides Search nav, Ingest button,
OpenSearch badge, indexed badges/filters when disabled)
- Skip ingestion polling when flag is off
- Make OpenSearch + embedding optional in docker-compose via profiles
- Add docker-compose.ingestion.yml override for full-stack ingestion
- Set BATCH_PAGE_SIZE=5 default in Docker local image
- Lead Quick Start with one-liner docker run command
- Document ingestion as opt-in with dedicated section
- Add BATCH_PAGE_SIZE, MAX_FILE_SIZE_MB, MAX_PAGE_COUNT, RATE_LIMIT_RPM to config tables
- Update test counts (380 backend, 159 frontend)
- Date CHANGELOG 0.4.0, bump frontend version to 0.4.0
- Sync CONTRIBUTING.md with E2E Karate test sections
Closes#180
Backend: GET /api/ingestion/search?q=…&doc_id=… endpoint with
SearchResponse schema. Frontend: search bar in Documents page, results
with filename, page, chunk index, relevance score. 3 new API tests.
Visual stepper in Studio topbar: Embedding → Indexing → Done.
Each step shows pending/active/done with animated dot. Store tracks
currentStep through the pipeline. Auto-resets after 2s.
- local_converter.py: remove redundant `_default_converter = None` in
except block of `_ensure_default_converter` (variable was already None,
re-raised immediately — dead store)
- test_analysis_service.py: replace bare `await task` with
`await asyncio.gather(task)` to satisfy static analysis
- Replace French mode strings (configurer/verifier/preparer) with English
equivalents (configure/verify/prepare) in StudioPage.vue and tests
- Extract _build_conversion_options, _run_conversion, _finalize_analysis
from _run_analysis_inner to respect Single Responsibility Principle
- Rename _get_default_converter to _ensure_default_converter to reflect
its lazy-init side effect
Closes#136, closes#137, closes#138
Frontend decoupling:
- Create shared/appConfig.ts as reactive bridge (locale, maxFileSizeMb,
maxPageCount) eliminating shared→features and feature→feature imports
- Give history feature its own Pinia store and API layer (was re-export
of analysis store)
- Give chunking feature its own Pinia store and API layer (was importing
from analysis)
- ChunkPanel receives analysis data via props instead of cross-feature
store import
- document/store reads maxFileSizeMb from shared config instead of
importing feature-flags store
- shared/i18n reads locale from shared config instead of importing
settings store
Backend:
- Add HealthResponse Pydantic schema for /api/health endpoint
Closes#140, closes#141, closes#142, closes#143
Add state-machine guard clauses to AnalysisJob transition methods
(mark_running, mark_completed, mark_failed, update_progress) to prevent
invalid status transitions. Make all domain value objects immutable with
frozen=True. Add 11 tests covering guard clause behavior.
Closes#132, closes#133
- Add DocumentRepository and AnalysisRepository protocols in domain/ports.py (#128)
- Refactor persistence repos from module functions to SqliteDocumentRepository
and SqliteAnalysisRepository classes
- Inject repos into AnalysisService and new DocumentService class via
constructor, removing direct imports of persistence and infra.settings (#129)
- Move _merge_results, _classify_error, _extract_html_body to domain/services.py (#130)
- Update main.py composition root to build and wire all dependencies
- Switch api/documents.py to Depends pattern matching api/analyses.py
- Update all tests to use injected mocks instead of module-level patches
Closes#128, closes#129, closes#130
Re-read the job from DB before mark_completed so that
progress_current/progress_total written during batched conversion
are not overwritten by the stale in-memory object.
Add regression unit test and e2e assertion on final progress values.
Set up a full E2E test suite (39 scenarios) using Karate against
the real API stack. Hybrid architecture: domain-based features +
cross-domain workflows, with data-driven testing and callable helpers.
Structure:
- e2e/pom.xml: Maven + karate-core 1.5
- 3 helpers (upload, analyze+poll, cleanup)
- 3 JSON schemas (health, document, analysis)
- 12 feature files across health, documents, analyses, workflows
- Tags: @smoke (2), @regression (35), @e2e (2)
- generate-test-data.py: fpdf2-based PDF generation (no binaries)
Also adds:
- RATE_LIMIT_RPM env var to make rate limiter configurable (0=disabled)
- CI job e2e with needs: [backend, frontend]
- e2e/ in .dockerignore
Closes#119
